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学生对 Coursera Project Network 提供的 Get Familiar with ML basics in a Kaggle Competition 的评价和反馈

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14 个评分
3 条评论

课程概述

In this 1-hour long project, you will be able to understand how to predict which passengers survived the Titanic shipwreck and make your first submission in an Machine Learning competition inside the Kaggle platform. Also, you as a beginner in Machine Learning applications, will get familiar and get a deep understanding of how to start a model prediction using basic supervised Machine Learning models. We will choose classifiers to learn, predict, and make an Exploratory Data Analysis (also called EDA). At the end, you will know how to measure a model performance, and submit your model to the competition and get a score from Kaggle. This guided project is for beginners in Data Science who want to do a practical application using Machine Learning. You will get familiar with the methods used in machine learning applications and data analysis. In order to be successful in this project, you should have an account on the Kaggle platform (no cost is necessary). Be familiar with some basic Python programming, we will use numpy and pandas libraries. Some background in Statistics is appreciated, like as knowledge in probability, but it’s not a requirement....

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1 - Get Familiar with ML basics in a Kaggle Competition 的 4 个评论(共 4 个)

创建者 121910303051 V S T

Mar 6, 2021

Great to start with the basics but needed a little more explanation on libraries

创建者 Isara S

Sep 17, 2021

This is a really good guided project to start with Kaggle Competition. I learnt all the basics require to start with Kaggle.

创建者 Mustak A

Aug 5, 2021

Need to add some more explanation about Kaggle

创建者 Hideki O

Oct 22, 2021

This course should be called the basics of how to use JupyterLab rather than ML basics. The instructor goes through some rudimentary data preprocessing, but there is very little theoretical explanation as to why the preprocessing should be done, and for a beginner it would be difficult to understand why the instructor did that. For example, there was no explanation as to why the "stratify" option was used when splitting the training and test data with the train_test_split() function. I was able to figure out the meaning of the option and why it matters by Google it, but I think it should have been explained in the lecture. This is just one example. Overall, there was too little explanation of the theoretical background in this class.